Drug target interaction prediction method based on multilayer network representation learning

Abstract

The invention discloses a drug target interaction prediction method based on multilayer network representation learning, and mainly solves the problem of low prediction accuracy in the prior art. Themethod comprises the following steps: downloading data from a drug and protein database, and respectively constructing multilayer similarity networks of drugs and proteins; calculating diffusion states of the two similarity networks respectively, and integrating the diffusion states respectively to obtain feature vectors of drugs and proteins; taking known drug target interaction data as supervision information, putting the drug and protein feature vectors into the same drug target space, and respectively obtaining projection matrixes of the drug and the protein by using a bilinear function; obtaining a prediction score matrix of drug target interaction according to the two projection matrixes and ranking the prediction score matrix; regarding eight top-ranked unknown drug target pairs aspotential drug target interactions. According to the method, the prediction accuracy of drug target interaction is improved, and the method can be used for predicting candidates of drug target pairs.

Description

technical field [0001] The invention belongs to the technical field of biological information, and in particular relates to a drug-target interaction prediction method, which can be used in drug repositioning experiments to provide candidate drug-target interactions. Background technique [0002] Drug targets refer to biomacromolecules that have pharmacological functions in the body and can be affected by drugs, such as certain proteins and nucleic acids and other biomacromolecules. The drug-target interaction refers to the combination of drug molecules and biological macromolecules in the human body, namely proteins, and play a role. If the drug is combined with different target proteins, the effect of the drug will be different. If the predicted drug target is related to a certain disease, the drug may have a potential therapeutic effect on the disease. [0003] Predicting the drug-target interaction is an important step in drug repositioning. Its purpose is to predict th...

AI Technical Summary

Problems solved by technology

[0016] However, these methods for integrating multiple types of data do not integrate data well

First, the diffusion state is directly used as a feature or prediction score through network fusion, which is easily affected by noise in different networks.

Second, integrating multiple networks into one network or throwing them into a common subspace may result in the loss of information specific to different networks, since the information from multiple data sources is mixed and cannot be distinguished

These factors will affect the accuracy of predicting drug-target interactions

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